Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analysis, create meaningful data visualizations, predict future trends from data, and more! Topics covered: 1) Importing Datasets 2) Cleaning the Data 3) Data frame manipulation 4) Summarizing the Data 5) Building machine learning Regression models 6) Building data pipelines Data Analysis with Python will be delivered through lecture, lab, and assignments. It includes following parts: Data Analysis libraries: will learn to use Pandas, Numpy and Scipy libraries to work with a sample dataset. We will introduce you to pandas, an open-source library, and we will use it to load, manipulate, analyze, and visualize cool datasets. Then we will introduce you to another open-source library, scikit-learn, and we will use some of its machine learning algorithms to build smart models and make cool predictions. If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge. LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate.
Importing and Exporting Data in Python
Loading...
来自 IBM 的课程
Data Analysis with Python
3115 个评分
从本节课中
Importing Datasets
与讲师见面
Joseph SantarcangeloPh.D., Data Scientist at IBM
IBM Developer Skills Network
In this video, we'll look at how to read
any data using python's pandas package.
Once we have our data in Python,
then we can perform all the subsequent data analysis procedures we need.
Data acquisition is a process of loading
and reading data into notebook from various sources.
To read any data using Python's pandas package,
there are two important factors to consider,
format and file path.
Format is the way data is encoded.
We can usually tell different encoding schemes
by looking at the ending of the file name.
Some common encodings are: CSV,
JSON, XLSX, HDF and so forth.
The path tells us where the data is stored.
Usually, it is stored either on the computer
we are using or online on the internet.
In our case, we found a dataset of used cars
which was obtained from the web address shown on the slide.
When Jerry entered the web address in his web browser,
he saw something like this.
Each row is one datapoint.
A large number of properties are associated with each datapoint.
Because the properties are separated from each other by commas,
we can guess the data format is CSV
which stands for comma separated values.
At this point, these are just numbers and don't mean much to humans,
but once we read in this data we can try to make more sense out of it.
In pandas, the read_CSV method can read in files with
columns separated by commas into a pandas data frame.
Reading data in pandas can be done quickly in three lines.
First, import pandas then define a variable with
a file path and then use the read_ CSV method to import the data.
However, read_CSV assumes the data contains a header.
Our data on used cars has no column headers.
So, we need to specify read_CSV to
not assign headers by setting header to none.
After reading the dataset,
it is a good idea to look at the data frame to get
a better intuition and to ensure that
everything occurred the way you expected.
Since printing the entire dataset may take up
too much time and resources to save time,
we can just use dataframe.head to show the first n rows of the data frame.
Similarly, dataframe.tail shows the bottom end rows of data frame.
Here, we printed out the first five rows of data.
It seems that the dataset was read successfully.
We can see that pandas automatically set the column header as a list of
integers because we set header equals none when we read the data.
It is difficult to work with the data frame
without having meaningful column names.
However, we can assign column names in pandas.
In our present case,
it turned out that we have the column names in a separate file on line.
We first put the column names in a list called headers,
then we set df.columns equals
headers to replace the default integer headers by the list.
If we use the head method
introduced in the last slide to check the dataset,
we see the correct headers inserted at the top of each column.
At some point in time,
after you've done operations on your dataframe you may
want to export your pandas dataframe to a new CSV file.
You can do this using the method to_CSV.
To do this, specify the file path
which includes the file name that you want to write to.
For example, if you would like to save dataframe
df as automobile.CSV to your own computer,
you can use the syntax df.to_CSV.
For this course, we will only read and save CSV files.
However, pandas also supports importing and
exporting of most data file types with different dataset formats.
The code syntax for reading and saving other data formats
is very similar to read or save CSV file.
Each column shows a different method to read and
save files into a different format.
